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Machine Learning Solutions

Practical ML projects that start with clear business questions and end with models that can be monitored, trusted, and improved over time.

Where machine learning fits

We focus on use‑cases where ML meaningfully improves decisions: forecasting, prioritisation, and pattern detection that would be difficult to capture in rules alone.

  • Demand and revenue forecasting
  • Lead and customer scoring
  • Recommendation and next‑best‑action engines
  • Anomaly detection for fraud, quality, or operations

Approach

We keep the modelling stack lightweight and transparent so your team can operate it after handover.

  • Data discovery and baseline analysis
  • Model design and experiment tracking
  • Monitoring and retraining workflows

Sample case studies

Case Study

Forecasting cash collections for a finance team

A finance department needed a more reliable view of expected cash‑in to plan investments and staffing.

  • Analysed historical payment behaviour across customers and regions
  • Built time‑series models with scenario overlays from the business
  • Exposed forecasts via dashboards and Excel exports

Impact: improved forecast accuracy, fewer last‑minute surprises, and more confident planning.

We built scenario sliders so finance could explore optimistic, base, and conservative views without touching the underlying models.

Discuss ML forecasting for your team
Case Study

Prioritising customer outreach with churn scoring

A subscription business wanted to focus customer success on accounts most at risk of leaving.

  • Joined product usage, support, and billing data into a unified dataset
  • Trained a churn‑propensity model and calibrated thresholds with the team
  • Integrated scores into existing workflows and dashboards

Impact: higher retention in the most at‑risk segments with targeted, proactive outreach.

Scores were surfaced directly in the tools the team already used, so they didn’t need to learn a new interface to act on insights.

Explore churn or propensity models